Cellular and molecular bioengineering最新文献

Introduction: Scarring from traumatic injury, burns, and other complications remains a significant problem that diminishes quality of life for millions of people worldwide. A common target for the development of new therapies to promote healing and reduce scarring are myofibroblasts because of their central role in pathological scarring. Recent work indicates that adipocyte lineage cells also contribute to the wound healing process, including clinical reports that indicate that the placement of autologous adipose micrografts at the surgical site improves the appearance and pliability of existing scars.

Methods: To better understand how adipocyte lineage cells interact with fibroblasts to promote healing, we first utilized an in vitro model of wound healing to visualize fibroblast spheroid collagen deposition via time-lapse imaging. We then introduced pre-adipocyte and adipocyte spheroids to visualize pair-wise spheroid interactions and collagen deposition among all three cell types. Finally, we quantified differences in the extracellular matrix (ECM) proteins produced using liquid chromatography with tandem mass spectrometry (LC-MS/MS).

Results: We found that all three cell-types contribute to ECM deposition and that the composition of the ECM proteins, or matrisome, was significantly different depending on which cells were co-cultured together.

Conclusions: By better understanding the interactions among these cell types, novel adipose-tissue-based therapeutic approaches can be developed to improve wound healing and reduce scar tissue.

Supplementary information: The online version contains supplementary material available at 10.1007/s12195-024-00829-8.

Introduction: Coaxial 3D bioprinting has advanced the formation of tissue constructs that recapitulate key architectures and biophysical parameters for in-vitro disease modeling and tissue-engineered therapies. Controlling gene expression within these structures is critical for modulating cell signaling and probing cell behavior. However, current transfection strategies are limited in spatiotemporal control because dense 3D scaffolds hinder diffusion of traditional vectors. To address this, we developed a coaxial extrusion 3D bioprinting technique using ultrasound-responsive gene delivery bioinks. These bioink materials incorporate echogenic microbubble gene delivery particles that upon ultrasound exposure can sonoporate cells within the construct, facilitating controllable transfection.

Methods: Phospholipid-coated gas-core microbubbles were electrostatically coupled to reporter transgene plasmid payloads and incorporated into cell-laden alginate bioinks at varying particle concentrations. These bioinks were loaded into the coaxial nozzle core for extrusion bioprinting with CaCl₂ crosslinker in the outer sheath. Resulting bioprints were exposed to 2.25 MHz focused ultrasound and evaluated for microbubble activation and subsequent DNA delivery and transgene expression.

Results: Coaxial printing parameters were established that preserved the stability of ultrasound-responsive gene delivery particles for at least 48 h in bioprinted alginate filaments while maintaining high cell viability. Successful sonoporation of embedded cells resulted in DNA delivery and robust ultrasound-controlled transgene expression. The number of transfected cells was modulated by varying the number of focused ultrasound pulses applied. The size region over which DNA was delivered was modulated by varying the concentration of microbubbles in the printed filaments.

Conclusions: Our results present a successful coaxial 3D bioprinting technique designed to facilitate ultrasound-controlled gene delivery. This platform enables remote, spatiotemporally-defined genetic manipulation in coaxially bioprinted tissue constructs with important applications for disease modeling and regenerative medicine.

Supplementary information: The online version contains supplementary material available at 10.1007/s12195-024-00818-x.

Introduction: Vascular smooth muscle cells (VSMCs) in the normal aortic wall regulate vascular contraction and dilation. VSMCs change their phenotype from contractile to synthetic and actively remodel the aortic wall under pathological conditions. Findings on the differentiation mechanism of VSMCs have been reported in many in vitro studies; however, the mechanical environments in vivo aortic walls are quite different from those of in vitro culture conditions: VSMCs in vivo exhibit an elongated shape and form a tissue that aligns with the circumferential direction of the walls, whereas VSMCs in vitro spread randomly and form irregular shapes during cultivation on conventional flat culture dishes and dedifferentiate into a synthetic phenotype. To clarify the mechanisms underlying the VSMC differentiation, it is essential to develop a cell culture model that considers the mechanical environment of in vivo aortic walls.

Methods: We fabricated a polydimethylsiloxane-based microgrooved substrate with 5, 10, and 20 μm of groove width and 5 μm of groove depth to induce VSMC elongation and alignment as observed in vivo. We established a coating method to control cell adhesion proteins only on the surface of groove concavities and investigated the effects of mechanical trapping of the cell nucleus in microgroove concavities on the morphology of intracellular nuclei, cell proliferation and motility, and VSMC differentiation.

Results: We found that VSMCs adhering to the concavities formed a uniform cell tissue and allowed remarkable elongation. In particular, the microgrooves with 5 μm of groove width and depth facilitated a significant nuclear deformation and volume reduction of the nucleus due to a lateral compression by the side wall of the groove concavities that is relatively similar to a sandwich-like arrangement of in vivo elastic lamellae, resulting in the drastic inhibition of cell motility and proliferation, and the significant improvement of VSMC differentiation.

Conclusions: The results indicate that mechanical trapping of the cell nucleus into microgroove concavity but not on convexity induces cell tissue growth and VSMC differentiation. Our cell culture model with microgrooved substrates can be useful for studying the mechanisms of VSMC differentiation, considering the in vivo vascular mechanical environment.

Supplementary information: The online version contains supplementary material available at 10.1007/s12195-024-00827-w.

Introduction: Conway's Game of Life (GOL), and related cellular automata (CA) models, have served as interesting simulations of complex behaviors resulting from simple rules of interactions between neighboring cells, that sometime resemble the growth and reproduction of living things. Thus, CA has been applied towards understanding the interaction and reproduction of single-cell organisms, and the growth of larger, disorganized tissues such as tumors. Surprisingly, however, there have been few attempts to adapt simple CA models to recreate the evolution of either new species, or subclones within a multicellular, tumor-like tissue.

Methods: In this article, I present a modified form of the classic Conway's GOL simulation, in which the three integer thresholds that define GOL (number of neighboring cells, below which a cell will "die of loneliness"; number of neighboring cells, above which a cell will die of overcrowding; and number of neighboring cells that will result in spontaneous birth of a new cell within an empty lattice location) are occasionally altered with a randomized mutation of fractional magnitude during new "cell birth" events. Newly born cells "inherit" the current mutation state of a neighboring parent cell, and over the course of 10,000 generations these mutations tend to accumulate until they impact the behaviors of individual cells, causing them to transition from the sparse, small patterns of live cells characteristic of GOL into a more dense, unregulated growth resembling a connected tumor tissue.

Results: The mutation rate and mutation magnitude were systematically varied in repeated randomized simulation runs, and it was determined that the most important mutated rule for the transition to unregulated, tumor-like growth was the overcrowding threshold, with the spontaneous birth and loneliness thresholds being of secondary importance. Spatial maps of the different "subclones" of cells that spontaneously develop during a typical simulation trial reveal that cells with greater fitness will overgrow the lattice and proliferate while the less fit, "wildtype" GOL cells die out and are replaced with mutant cells.

Conclusions: This simple modeling approach can be easily modified to add complexity and more realistic biological details, and may yield new understanding of cancer and somatic evolution.

Supplementary information: The online version contains supplementary material available at 10.1007/s12195-024-00828-9.

Background: Kidney cancer is a significant global health problem that affects nearly 1 in 25 of cancer patients. Prevalence, morbidity and mortality data associated with kidney cancer continue to increase every year, revealing a heavy economic and social burden. Organoid culture is a new research tool with great potential for many applications, particularly in cancer research. The integration of organoids with other emerging technologies has simultaneously expanded their potential applications. However, there is no thorough assessment of organoids in the field of renal cancer research.

Objectives: This paper presents a comprehensive review of the current development of renal cancer organoids and discusses the corresponding solutions and future directions of renal cancer organoids.

Methods: In this study, we have compared the operating procedures of different organoid culture protocols and proposed a summary of constituents in culture media. Extensive discussions of renal cancer organoids, including generation and maintenance approaches, application scenarios, current challenges and prospects, have also been made. The information required for this study is extracted from literature databases such as PubMed, SCOPUS and Web of Science.

Results: In this article, we systematically review thirteen successful methods for generating organoids to kidney cancer and provide practical guidelines for their construction as a reference. In addition, we also elucidate the clinical application of organoids, address the existing challenges and limitations, and highlight promising prospects.

Conclusion: Ultimately, we firmly believe that as kidney tumour organoids continue to develop and improve, they will become a crucial tool for treating kidney cancer.

Purpose: In many diseases, an overabundance of macrophages contributes to adverse outcomes. While numerous studies have compared macrophage phenotype after mechanical stimulation or with varying local stiffness, it is unclear if and how macrophages directly contribute to mechanical forces in their microenvironment.

Methods: Raw 264.7 murine macrophages were embedded in a confining agarose gel, and proliferated to form spheroids over days/weeks. Gels were synthesized at various concentrations to tune stiffness and were shown to support cell viability and spheroid growth. These cell-agarose constructs were treated with media supplements to promote macrophage polarization. Spheroid geometries were used to computationally model the strain generated in the agarose by macrophage spheroid growth. Agarose-embedded macrophages were analyzed for viability, spheroid size, stress generation, and gene expression.

Results: Macrophages form spheroids and generate growth-induced mechanical forces (i.e., solid stress) within confining agarose gels, which can be maintained for at least 16 days in culture. Increasing agarose concentration increases gel stiffness, restricts spheroid expansion, limits gel deformation, and causes a decrease in Ki67 expression. Lipopolysaccharide (LPS) stimulation increases spheroid growth, though this effect is reversed with the addition of IFNγ. The mechanosensitive ion channels Piezo1 and TRPV4 have reduced expression with increased stiffness, externally applied compression, LPS stimulation, and M1-like polarization.

Conclusions: Macrophages alone both respond to and generate solid stress. Understanding how macrophage generation of growth-induced solid stress responds to different environmental conditions will help to inform treatment strategies for the plethora of diseases that involve macrophage accumulation and inflammation.

Supplementary information: The online version contains supplementary material available at 10.1007/s12195-024-00824-z.

Introduction: Fibroblasts, an abundant cell type in the breast tumor microenvironment, interact with cancer cells and orchestrate tumor progression and drug resistance. However, the mechanisms by which fibroblast-derived factors impact drug sensitivity remain poorly understood. Here, we develop rational combination therapies that are informed by proteomic profiling to overcome fibroblast-mediated therapeutic resistance in HER2+ breast cancer cells.

Methods: Drug sensitivity to the HER2 kinase inhibitor lapatinib was characterized under conditions of monoculture and exposure to breast fibroblast-conditioned medium. Protein expression was measured using reverse phase protein arrays. Candidate targets for combination therapy were identified using differential expression and multivariate regression modeling. Follow-up experiments were performed to evaluate the effects of HER2 kinase combination therapies in fibroblast-protected cancer cell lines and fibroblasts.

Results: Compared to monoculture, fibroblast-conditioned medium increased the expression of plasminogen activator inhibitor-1 (PAI1) and cell cycle regulator polo like kinase 1 (PLK1) in lapatinib-treated breast cancer cells. Combination therapy of lapatinib with inhibitors targeting either PAI1 or PLK1, eliminated fibroblast-protected cancer cells, under both conditions of direct coculture with fibroblasts and protection by fibroblast-conditioned medium. Analysis of publicly available, clinical transcriptomic datasets revealed that HER2-targeted therapy fails to suppress PLK1 expression in stroma-rich HER2+ breast tumors and that high PAI1 gene expression associates with high stroma density. Furthermore, we showed that an epigenetics-directed approach using a bromodomain and extraterminal inhibitor to globally target fibroblast-induced proteomic adaptions in cancer cells, also restored lapatinib sensitivity.

Conclusions: Our data-driven framework of proteomic profiling in breast cancer cells identified the proteolytic degradation regulator PAI1 and the cell cycle regulator PLK1 as predictors of fibroblast-mediated treatment resistance. Combination therapies targeting HER2 kinase and these fibroblast-induced signaling adaptations eliminates fibroblast-protected HER2+ breast cancer cells.

Supplementary information: The online version contains supplementary material available at 10.1007/s12195-024-00823-0.

Purpose: The primary aim of this study was to develop an open-source Python-based software for the automated analysis of dynamic cell behaviors in microphysiological models using non-confocal microscopy. This research seeks to address the existing gap in accessible tools for high-throughput analysis of endothelial tube formation and cell invasion in vitro, facilitating the rapid assessment of drug sensitivity.

Methods: Our approach involved annotating over 1000 2 mm Z-stacks of cancer and endothelial cell co-culture model and training machine learning models to automatically calculate cell coverage, cancer invasion depth, and microvessel dynamics. Specifically, cell coverage area was computed using focus stacking and Gaussian mixture models to generate thresholded Z-projections. Cancer invasion depth was determined using a ResNet-50 binary classification model, identifying which Z-planes contained invaded cells and measuring the total invasion depth. Lastly, microvessel dynamics were assessed through a U-Net Xception-style segmentation model for vessel prediction, the DisPerSE algorithm to extract an embedded graph, then graph analysis to quantify microvessel length and connectivity. To further validate our software, we reanalyzed an image set from a high-throughput drug screen involving a chemotherapy agent on a 3D cervical and endothelial co-culture model. Lastly, we applied this software to two naive image datasets from coculture lumen and microvascular fragment models.

Results: The software accurately measured cell coverage, cancer invasion, and microvessel length, yielding drug sensitivity IC₅₀ values with a 95% confidence level compared to manual calculations. This approach significantly reduced the image processing time from weeks down to h. Furthermore, the software was able to calculate cell coverage, microvessel length, and invasion depth from two additional microphysiological models that were imaged with confocal microscopy, highlighting the versatility of the software.

Conclusions: Our free and open source software offers an automated solution for quantifying 3D cell behavior in microphysiological models assessed using non-confocal microscopy, providing the broader Cellular and Molecular Bioengineering community with an alternative to standard confocal microscopy paired with proprietary software.This software can be found in our GitHub repository: https://github.com/fogg-lab/tissue-model-analysis-tools.

Supplementary information: The online version contains supplementary material available at 10.1007/s12195-024-00821-2.

Purpose: Current bulk molecular assays fail to capture spatial signaling activities in cancers, limiting our understanding of drug resistance mechanisms. We developed a graph-based super-resolution protein-protein interaction (GSR-PPI) technique to spatially resolve single-cell signaling networks and evaluate whether higher resolution microscopy enhances the biological study of PPIs using deep learning classification models.

Methods: Single-cell spatial proximity ligation assays (PLA, ≤ 9 PPI pairs) were conducted on EGFR mutant (EGFRm) PC9 and HCC827 cells (>10,000 cells) treated with 100 nM Osimertinib. Multiplexed PPI images were obtained using wide-field and super-resolution microscopy (Zeiss Airyscan, SRRF). Graph-based deep learning models analyzed subcellular protein interactions to classify drug treatment states and test GSR-PPI on clinical tissue samples. GSR-PPI triangulated PPI nodes into 3D relationships, predicting drug treatment labels. Biological discriminative ability (BDA) was evaluated using accuracy, AUC, and F1 scores. The method was also applied to 3D spatial proteomic molecular pixelation (PixelGen) data from T cells.

Results: GSR-PPI outperformed baseline models in predicting drug responses from multiplexed PPI imaging in EGFRm cells. Super-resolution data significantly improved accuracy over localized wide-field imaging. GSR-PPI classified drug treatment states in cancer cells and human lung tissues, with performance improving as imaging resolution increased. It differentiated single and combination drug therapies in HCC827 cells and human tissues. Additionally, GSR-PPI accurately distinguished T-cell stimulation states, identifying key nodes such as CD44, CD45, and CD54.

Conclusion: The GSR-PPI framework provides valuable insights into spatial protein interactions and drug responses, enhancing the study of signaling biology and drug resistance.

Supplementary information: The online version contains supplementary material available at 10.1007/s12195-024-00822-1.